1. Technical Field
This disclosure relates to an object detection method, and more particularly to an enhanced object detection method, which uses image discontinuousness to improve the quality of object detection and to shorten the time required for object detection.
2. Related Art
For data process device, detecting humans or objects of specific class in an image involves numerical computation, different from human brain logics. Object detection is widely used in monitoring, automated machinery (robot), or environmental monitoring.
Take human detection as an illustration, body size, posture, clothing, image capturing angle, light, and image quality all influence appearance of people in images, so the detection can not be accomplished by simple comparison of image characteristics but by relatively complex computation mechanism.
In the above computation mechanism, the common used calculation basis is Histograms of Oriented Gradients (HOG) proposed by Dalal et al. HOG counts occurrences of gradient orientation of pixels in a localized portion of an image as the feature of the localized portion and then uses support vector machine to perform detection.
However, the requirement for resolution or clarity is relatively high for HOG, and the object may not be detected if the resolution is low or the edge of the object is blurred due to noises. Besides, often HOG doesn't detect the complete object but part of the object, the representative vector dimensions of the object in HOG are so large that the computation is heavy and that the HOG is difficult to used in real-time applications. Therefore, there is still room for the improvement of HOG, and improvement methods are continuously proposed as well.